Visualize the environment HumanoidBulletEnv-v0

import sys
import logging
import itertools
import time

import numpy as np
np.random.seed(0)
import gym
import pybullet_envs # This import registers the environment in gym
import pybullet as p

logging.basicConfig(level=logging.DEBUG,
        format='%(asctime)s [%(levelname)s] %(message)s',
        stream=sys.stdout, datefmt='%H:%M:%S')

Environment

env = gym.make("HumanoidBulletEnv-v0")
env.seed(0)
for key in vars(env):
    logging.info('%s: %s', key, vars(env)[key])
for key in vars(env.spec):
    logging.info('%s: %s', key, vars(env.spec)[key])


def adjust_camera():
    distance, yaw, pitch = 2, 0, -20
    num_bodies = p.getNumBodies()
    torse_ids = [body_id for body_id in range(num_bodies) if p.getBodyInfo(body_id)[0].decode("ascii") == "torso"]
    torse_id = torse_ids[0]
    position, orientation = p.getBasePositionAndOrientation(torse_id)
    p.resetDebugVisualizerCamera(distance, yaw, pitch, position)

00:06:09 [INFO] env: <HumanoidBulletEnv<HumanoidBulletEnv-v0>>
00:06:09 [INFO] action_space: Box(-1.0, 1.0, (17,), float32)
00:06:09 [INFO] observation_space: Box(-inf, inf, (44,), float32)
00:06:09 [INFO] reward_range: (-inf, inf)
00:06:09 [INFO] metadata: {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 60}
00:06:09 [INFO] _max_episode_steps: 1000
00:06:09 [INFO] _elapsed_steps: None
00:06:09 [INFO] id: HumanoidBulletEnv-v0
00:06:09 [INFO] entry_point: pybullet_envs.gym_locomotion_envs:HumanoidBulletEnv
00:06:09 [INFO] reward_threshold: None
00:06:09 [INFO] nondeterministic: False
00:06:09 [INFO] max_episode_steps: 1000
00:06:09 [INFO] _kwargs: {}
00:06:09 [INFO] _env_name: HumanoidBulletEnv

Agent

class Agent:
    def reset(self, mode):
        pass

    def step(self, observation, reward, done):
        weights0 = np.array([
                [+0.2289, +0.2584, +0.2595, +0.0173, +0.1293, -0.2980, +0.1410, +0.0982, +0.0216, +0.3724, +0.2204, -0.0734, -0.2420, -0.3443, -0.2738, -0.3825, +0.1504, -0.0930, -0.2680, +0.0685, +0.1592, +0.2534, -0.0787, -0.0426, +0.2591, +0.2134, -0.1631, +0.5168, +0.1444, +0.2736, +0.3623, -0.3472, +0.0393, -0.3056, +0.3850, -0.5231, +0.4511, +0.4223, -0.0905, +0.2265, +0.1662, +0.1092, +0.0426, -0.0209, +0.3260, -0.1788, +0.5045, -0.0254, +0.6684, +0.4659, +0.2193, +0.6121, -0.1771, +0.3024, +0.3233, -0.3380, +0.1834, +0.1947, +0.2840, -0.0212, +0.0610, +0.0254, -0.0687, +0.3342, -0.2010, +0.4851, -0.5739, -0.3228, -0.2242, +0.6149, +0.2704, -0.1006, +0.3950, -0.2684, +0.0090, -0.2419, +0.1112, -0.0795, +0.0021, -0.0317, +0.1345, -0.2847, +0.2323, +0.5374, -0.0119, -0.2098, +0.2074, +0.1693, +0.4537, -0.1453, +0.2661, -0.2997, +0.1043, -0.2340, +0.4472, -0.0415, +0.6437, +0.0279, +0.1609, +0.3353, -0.2240, -0.5433, -0.0053, +0.1863, +0.1038, +0.3337, +0.3889, +0.4159, -0.0836, -0.0826, +0.0872, -0.1362, +0.0061, -0.2982, -0.0074, -0.1452, -0.0655, -0.1369, -0.0493, -0.1082, -0.4080, +0.4732, +0.1229, +0.3087, -0.1222, +0.3846, +0.0719, -0.4536, +0.1202, -0.3903, -0.0445, +0.4052, +0.2922, +0.1095, +0.0317, +0.0974, +0.1149, -0.3794, +0.4364, -0.1597, +0.2889, +0.2431, +0.6867, +0.5117, -0.2517, +0.2835, -0.0365, +0.3303, +0.2569, +0.3636, -0.0945, +0.1853, -0.1341, +0.2595, -0.0174, -0.3227, +0.1999, +0.2348, +0.5826, -0.0480, +0.1449, +0.3866, +0.4740, +0.2486, +0.5011, -0.0334, +0.0953, +0.1072, -0.0901, -0.3235, +0.3258, +0.3003, +0.1229, -0.2257, -0.1920, -0.2025, -0.2022, +0.0843, +0.4563, -0.2654, +0.3158, -0.3102, +0.1105, -0.1171, +0.0465, -0.0692, +0.2824, +0.2598, -0.0289, -0.7774, +0.3501, -0.0635, +0.2257, -0.1644, +0.4091, -0.1967, +0.1614, +0.5468, +0.1365, +0.5054, +0.1986, +0.1368, +0.0886, -0.1305, +0.4297, +0.2482, -0.1914, -0.2909, +0.1784, +0.0690, +0.0224, +0.0378, +0.4991, -0.3410, +0.4281, -0.4574, +0.1952, -0.0444, -0.1939, +0.1637, +0.2385, +0.1354, +0.1817, -0.3849, +0.1909, +0.6585, +0.0997, +0.1376, +0.3598, +0.0611, +0.0124, -0.1492, -0.0659, -0.2671, -0.3925, +0.2333, +0.4184, -0.1364, -0.0361, -0.5937, -0.0327, -0.1142, -0.3334, +0.2963, +0.2350, -0.2600, +0.2784, -0.0720, -0.1594, -0.2652, +0.3922, +0.7291, -0.1830, +0.2253, +0.1965, +0.1972],
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                [-0.0193, -0.1029, +0.1858, +0.0139, +0.2608, -0.0146, +0.0154, +0.0489, -0.1093, +0.0104, -0.2219, +0.0648, -0.0656, -0.0877, +0.0203, -0.0899, -0.0903],
                [-0.0886, +0.1405, -0.3229, +0.1365, -0.0121, +0.0062, -0.0392, -0.0051, +0.1538, +0.2586, -0.0580, +0.1502, -0.0022, -0.0271, -0.0936, -0.4547, +0.0754],
                [+0.3475, +0.0164, -0.2164, +0.0795, -0.0293, +0.1575, +0.0395, -0.4194, +0.0051, -0.0399, +0.1034, +0.3149, -0.0858, -0.1606, -0.0197, -0.0434, -0.0950],
                [+0.1236, -0.0872, -0.2009, +0.3143, +0.1047, +0.0123, -0.1373, -0.0199, +0.0415, -0.0361, +0.4936, +0.1017, +0.0232, -0.2103, -0.0957, -0.1162, +0.0018],
                [-0.0303, +0.2271, +0.2671, -0.1139, +0.0505, +0.0459, +0.0181, -0.0978, +0.0442, +0.0428, -0.0411, +0.1537, -0.2310, -0.0133, +0.0393, +0.2811, +0.0302],
                [+0.0180, -0.2184, -0.0850, -0.1738, -0.1618, +0.0298, -0.0198, +0.3145, -0.1872, -0.0837, -0.0753, -0.1052, -0.0610, -0.1089, -0.0288, +0.0058, -0.0733],
                [+0.0005, +0.1507, +0.1555, +0.0516, -0.1457, -0.1738, -0.0570, +0.0876, +0.2975, +0.1416, +0.0454, -0.3029, +0.0148, -0.2664, +0.0913, +0.0374, +0.1604],
                [+0.0099, +0.1211, +0.3735, +0.2346, +0.0826, -0.0476, -0.0548, -0.0957, +0.0246, -0.0414, +0.0233, -0.0779, +0.0275, -0.1103, +0.1439, -0.1177, +0.0950],
                [+0.0991, -0.0663, -0.0226, -0.2599, -0.2676, -0.2060, -0.0898, +0.0218, -0.0629, +0.3991, -0.0857, -0.0919, -0.0442, +0.0097, -0.0634, -0.1714, +0.0120],
                [-0.0083, -0.2737, -0.0861, -0.1251, +0.0406, +0.0368, -0.1133, +0.1224, -0.1233, +0.0078, +0.1233, +0.0664, -0.2030, +0.0159, -0.4872, +0.0998, -0.1442],
                [-0.0557, +0.0044, +0.0650, -0.3940, +0.0112, +0.1438, +0.0244, -0.0566, -0.1197, +0.0153, -0.0472, -0.1162, -0.1096, -0.0067, +0.0748, -0.1948, +0.0299],
                [-0.1223, -0.1968, +0.0289, -0.1279, +0.2201, -0.2378, +0.0276, +0.2640, +0.0479, +0.0962, -0.0191, -0.2616, -0.1661, -0.1301, +0.1400, +0.0645, -0.1336],
                [+0.0914, +0.0268, +0.0498, +0.2482, -0.1405, -0.1721, +0.0066, +0.0427, +0.1062, -0.0699, +0.0238, +0.1893, +0.0670, -0.0401, -0.0636, +0.0611, -0.0596],
                [-0.1982, -0.2318, +0.1808, +0.0593, +0.1440, +0.3635, +0.0442, -0.1524, +0.0650, +0.0216, -0.0772, -0.1202, +0.2329, +0.0204, -0.0736, +0.2445, +0.0261],
                [-0.3588, +0.0298, -0.0034, -0.0458, +0.2777, +0.2485, +0.3801, -0.0695, -0.1679, -0.5362, -0.1887, +0.0389, +0.2512, -0.0413, +0.0365, -0.0915, +0.0519],
                [-0.0069, +0.1737, +0.1234, +0.0086, +0.0216, +0.1098, -0.0172, +0.0209, -0.1796, +0.1730, -0.0681, +0.1485, +0.1096, -0.0660, -0.0895, +0.1351, -0.0554],
                [-0.2458, +0.0149, +0.0155, -0.0099, +0.0206, +0.3378, +0.2285, +0.0385, -0.0532, +0.2575, -0.0545, +0.0151, +0.0540, -0.0833, +0.0946, +0.0446, -0.0862],
                [-0.1136, +0.1565, +0.1244, -0.0161, -0.0614, +0.1204, -0.0771, +0.2362, +0.0355, +0.2028, -0.0155, -0.0652, -0.2262, -0.0749, -0.1730, -0.0957, +0.2760],
                [+0.0634, -0.0011, -0.1770, -0.1426, +0.1599, +0.0892, -0.0261, +0.0790, +0.0812, +0.4281, +0.0143, +0.1554, +0.3770, -0.0206, -0.1564, +0.0818, -0.0514],
                [+0.0102, -0.1232, -0.2659, -0.1866, +0.0090, +0.2672, -0.0540, +0.1100, -0.2942, -0.2454, -0.0566, -0.2273, +0.1938, +0.0229, -0.1563, -0.0024, -0.0975],
                [-0.0645, -0.0004, +0.1548, +0.2129, -0.0429, -0.0628, +0.0306, +0.1769, -0.0491, -0.0285, -0.0621, -0.4293, +0.1821, +0.1154, -0.0027, +0.0264, -0.0107],
                [+0.3158, -0.0591, +0.0461, +0.0682, +0.0015, -0.0667, +0.1125, +0.0242, -0.0998, +0.0405, -0.1104, +0.0892, -0.1950, -0.0443, -0.1472, +0.1181, -0.0764],
                [-0.0697, -0.0853, +0.1715, +0.0623, +0.0484, -0.0205, -0.2043, -0.0463, -0.1115, +0.0058, -0.0556, -0.0808, +0.0454, -0.0959, +0.1666, +0.3646, +0.2677],
                [-0.1574, -0.2403, -0.1285, -0.0211, -0.2054, -0.0027, +0.1099, -0.1112, -0.2730, -0.1247, +0.1788, -0.1336, -0.1028, +0.1764, +0.2594, -0.0231, +0.1732],
                [+0.2124, +0.1167, -0.0895, -0.0324, +0.2043, +0.0680, -0.0162, -0.0062, -0.0897, +0.0854, +0.0748, +0.3693, -0.0414, +0.0972, +0.0094, +0.0354, +0.1289],
                [+0.2460, -0.1170, -0.1432, -0.0896, +0.0093, +0.0557, -0.0195, +0.0088, +0.0839, +0.0375, +0.1659, -0.0451, -0.1184, -0.2017, +0.0481, -0.1517, +0.1372],
                [-0.0602, +0.3316, -0.0452, -0.0173, +0.0148, +0.0459, +0.0167, -0.0842, +0.0428, +0.2873, -0.0527, +0.0752, -0.0441, +0.0583, +0.0443, +0.1112, -0.0272],
                [+0.0051, -0.0555, +0.1717, +0.1398, -0.1614, -0.0300, -0.1820, -0.0658, +0.0742, -0.0847, -0.1139, +0.0728, +0.1420, +0.2099, -0.2902, +0.1338, +0.1417],
                [-0.0094, -0.0020, -0.1126, -0.2572, -0.2766, +0.0049, +0.1369, +0.0054, -0.1328, +0.0361, +0.1361, +0.3090, +0.0047, +0.1695, +0.0896, -0.1901, -0.1786],
                [+0.1339, -0.0886, +0.0770, +0.0189, -0.1017, -0.0070, +0.1551, -0.0489, +0.0966, -0.0131, -0.0856, -0.1183, +0.1585, -0.0139, +0.1861, -0.1487, +0.0005],
                [+0.2260, +0.0098, -0.3146, -0.0441, +0.1948, +0.0520, +0.0863, -0.0021, -0.1183, +0.0328, +0.0124, +0.0056, +0.0944, -0.0319, +0.0679, +0.0774, +0.0409],
                [-0.0237, -0.3453, -0.1483, -0.2272, +0.1675, -0.1041, -0.0126, +0.3376, -0.0138, +0.1525, -0.0322, +0.2070, -0.2725, -0.0600, -0.1466, -0.2563, +0.0551],
                [+0.1966, -0.0838, -0.0537, +0.0933, -0.0060, -0.0721, -0.0399, -0.1153, -0.1987, -0.0413, +0.2588, +0.0648, -0.0473, +0.1387, +0.6180, +0.0229, -0.2610],
                [-0.1566, -0.0589, -0.0600, +0.0966, +0.0738, -0.0744, -0.0010, +0.0253, +0.2387, -0.0539, +0.0625, +0.1197, +0.0486, +0.0605, +0.2775, -0.0594, -0.0759],
                [-0.4288, -0.0693, +0.0067, +0.3567, +0.0813, -0.1369, -0.3431, +0.0559, -0.0320, +0.0158, -0.1947, -0.3235, +0.1294, -0.2915, -0.2301, -0.1980, +0.0892],
                [-0.1741, +0.0445, -0.0525, +0.0650, -0.0474, +0.1845, -0.0831, -0.0693, -0.0071, -0.0772, +0.0915, -0.0571, -0.1035, -0.0038, -0.1508, -0.2323, -0.1788],
                [+0.1543, -0.0994, +0.0674, +0.1953, +0.1254, -0.1263, +0.0854, -0.1048, +0.0106, -0.0203, +0.1019, -0.0881, +0.2788, -0.2237, -0.0723, +0.0393, -0.0969],
                [+0.0560, +0.2425, +0.1031, +0.1155, -0.1187, -0.1155, -0.0789, +0.2113, +0.0024, +0.2648, -0.1811, -0.0259, -0.1363, -0.2149, -0.2199, -0.1105, -0.1616],
                [-0.0967, +0.0007, -0.2230, +0.1197, +0.1085, +0.0267, -0.0633, +0.1611, -0.0078, +0.0088, -0.0879, -0.1362, +0.1364, -0.1784, +0.1725, -0.0491, -0.0177],
                [+0.0724, -0.0574, +0.0696, -0.0095, -0.1601, +0.1243, +0.2373, -0.1058, +0.1610, -0.1874, -0.0689, +0.1806, -0.2117, -0.0855, -0.0657, +0.0366, -0.0797],
                ])
        bias2 = np.array([-0.0356, +0.0776, -0.0344, +0.1375, +0.1048, +0.3648, +0.3240, +0.1319, +0.1161, +0.3373, +0.3193, +0.0120, +0.0253, -0.2434, -0.1291, +0.1042, -0.2448])

        hidden1 = (np.dot(observation, weights0) + bias0).clip(min=0)
        hidden2 = (np.dot(hidden1, weights1) + bias1).clip(min=0)
        action = np.dot(hidden2, weights2) + bias2
        return action

    def close(self):
        pass


agent = Agent()

Test and Visualize

def play_episode(env, agent, max_episode_steps=None, mode=None, render=False):
    if render:
        env.render(mode="human")
    observation, reward, done = env.reset(), 0., False
    agent.reset(mode=mode)
    episode_reward, elapsed_steps = 0., 0
    while True:
        if render:
            adjust_camera()
            env.render(mode="human")
            time.sleep(0.02)
        action = agent.step(observation, reward, done)
        if done:
            break

        observation, reward, done, _ = env.step(action)
        episode_reward += reward
        elapsed_steps += 1
        if max_episode_steps and elapsed_steps >= max_episode_steps:
            break
    agent.close()
    return episode_reward, elapsed_steps


logging.info('==== test ====')
episode_rewards = []
for episode in range(100):
    episode_reward, elapsed_steps = play_episode(env, agent, render=True)
    episode_rewards.append(episode_reward)
    logging.debug('test episode %d: reward = %.2f, steps = %d',
            episode, episode_reward, elapsed_steps)
logging.info('average episode reward = %.2f ± %.2f',
        np.mean(episode_rewards), np.std(episode_rewards))

00:06:11 [INFO] ==== test ====
00:07:09 [DEBUG] test episode 0: reward = 3627.15, steps = 1000
00:08:11 [DEBUG] test episode 1: reward = 3600.06, steps = 1000
00:09:12 [DEBUG] test episode 2: reward = 3601.64, steps = 1000
00:10:15 [DEBUG] test episode 3: reward = 3649.76, steps = 1000
00:11:17 [DEBUG] test episode 4: reward = 3582.52, steps = 1000
00:12:18 [DEBUG] test episode 5: reward = 3589.11, steps = 1000
00:13:17 [DEBUG] test episode 6: reward = 3598.95, steps = 1000
00:14:17 [DEBUG] test episode 7: reward = 3609.17, steps = 1000
00:15:16 [DEBUG] test episode 8: reward = 3589.29, steps = 1000
00:16:16 [DEBUG] test episode 9: reward = 3627.74, steps = 1000
00:17:15 [DEBUG] test episode 10: reward = 3546.57, steps = 1000
00:18:20 [DEBUG] test episode 11: reward = 3624.00, steps = 1000
00:18:24 [DEBUG] test episode 12: reward = 19.42, steps = 67
00:19:25 [DEBUG] test episode 13: reward = 3602.72, steps = 1000
00:20:26 [DEBUG] test episode 14: reward = 3614.99, steps = 1000
00:21:30 [DEBUG] test episode 15: reward = 3606.14, steps = 1000
00:22:31 [DEBUG] test episode 16: reward = 3618.07, steps = 1000
00:23:32 [DEBUG] test episode 17: reward = 3627.71, steps = 1000
00:23:33 [DEBUG] test episode 18: reward = -3.76, steps = 22
00:24:33 [DEBUG] test episode 19: reward = 3626.20, steps = 1000
00:25:35 [DEBUG] test episode 20: reward = 3559.19, steps = 1000
00:25:37 [DEBUG] test episode 21: reward = -10.92, steps = 29
00:26:38 [DEBUG] test episode 22: reward = 3593.06, steps = 1000
00:26:41 [DEBUG] test episode 23: reward = 7.51, steps = 49
00:27:41 [DEBUG] test episode 24: reward = 3638.89, steps = 1000
00:28:42 [DEBUG] test episode 25: reward = 3603.43, steps = 1000
00:29:45 [DEBUG] test episode 26: reward = 3573.38, steps = 1000
00:30:47 [DEBUG] test episode 27: reward = 3630.98, steps = 1000
00:30:52 [DEBUG] test episode 28: reward = 61.78, steps = 79
00:31:56 [DEBUG] test episode 29: reward = 3600.64, steps = 1000
00:33:04 [DEBUG] test episode 30: reward = 3606.76, steps = 1000
00:34:10 [DEBUG] test episode 31: reward = 3642.70, steps = 1000
00:35:13 [DEBUG] test episode 32: reward = 3588.97, steps = 1000
00:36:16 [DEBUG] test episode 33: reward = 3617.01, steps = 1000
00:37:20 [DEBUG] test episode 34: reward = 3644.40, steps = 1000
00:37:22 [DEBUG] test episode 35: reward = -4.10, steps = 37
00:38:27 [DEBUG] test episode 36: reward = 3600.49, steps = 1000
00:39:30 [DEBUG] test episode 37: reward = 3626.75, steps = 1000
00:40:33 [DEBUG] test episode 38: reward = 3632.26, steps = 1000
00:41:37 [DEBUG] test episode 39: reward = 3591.52, steps = 1000
00:42:39 [DEBUG] test episode 40: reward = 3526.64, steps = 1000
00:43:42 [DEBUG] test episode 41: reward = 3626.18, steps = 1000
00:43:44 [DEBUG] test episode 42: reward = 3.92, steps = 33
00:44:48 [DEBUG] test episode 43: reward = 3604.68, steps = 1000
00:45:52 [DEBUG] test episode 44: reward = 3603.97, steps = 1000
00:46:56 [DEBUG] test episode 45: reward = 3564.37, steps = 1000
00:47:58 [DEBUG] test episode 46: reward = 3590.12, steps = 1000
00:49:00 [DEBUG] test episode 47: reward = 3624.21, steps = 1000
00:50:02 [DEBUG] test episode 48: reward = 3630.99, steps = 1000
00:51:04 [DEBUG] test episode 49: reward = 3610.88, steps = 1000
00:52:06 [DEBUG] test episode 50: reward = 3611.62, steps = 1000
00:53:08 [DEBUG] test episode 51: reward = 3557.13, steps = 1000
00:54:10 [DEBUG] test episode 52: reward = 3594.21, steps = 1000
00:55:12 [DEBUG] test episode 53: reward = 3612.09, steps = 1000
00:56:13 [DEBUG] test episode 54: reward = 3619.13, steps = 1000
00:57:13 [DEBUG] test episode 55: reward = 3632.19, steps = 1000
00:58:12 [DEBUG] test episode 56: reward = 3627.03, steps = 1000
00:59:12 [DEBUG] test episode 57: reward = 3613.71, steps = 1000
00:00:13 [DEBUG] test episode 58: reward = 3613.01, steps = 1000
00:00:18 [DEBUG] test episode 59: reward = 32.05, steps = 86
00:01:18 [DEBUG] test episode 60: reward = 3568.49, steps = 1000
00:02:19 [DEBUG] test episode 61: reward = 3611.76, steps = 1000
00:03:19 [DEBUG] test episode 62: reward = 3623.30, steps = 1000
00:04:18 [DEBUG] test episode 63: reward = 3620.09, steps = 1000
00:05:18 [DEBUG] test episode 64: reward = 3626.96, steps = 1000
00:06:19 [DEBUG] test episode 65: reward = 3611.29, steps = 1000
00:07:20 [DEBUG] test episode 66: reward = 3595.89, steps = 1000
00:07:31 [DEBUG] test episode 67: reward = 270.03, steps = 173
00:08:31 [DEBUG] test episode 68: reward = 3608.21, steps = 1000
00:09:31 [DEBUG] test episode 69: reward = 3611.99, steps = 1000
00:10:31 [DEBUG] test episode 70: reward = 3632.10, steps = 1000
00:11:32 [DEBUG] test episode 71: reward = 3600.27, steps = 1000
00:12:34 [DEBUG] test episode 72: reward = 3527.29, steps = 1000
00:13:36 [DEBUG] test episode 73: reward = 3604.33, steps = 1000
00:13:41 [DEBUG] test episode 74: reward = 50.15, steps = 77
00:14:43 [DEBUG] test episode 75: reward = 3608.72, steps = 1000
00:15:46 [DEBUG] test episode 76: reward = 3623.27, steps = 1000
00:16:49 [DEBUG] test episode 77: reward = 3619.39, steps = 1000
00:17:51 [DEBUG] test episode 78: reward = 3617.87, steps = 1000
00:18:52 [DEBUG] test episode 79: reward = 3637.79, steps = 1000
00:19:54 [DEBUG] test episode 80: reward = 3600.80, steps = 1000
00:20:57 [DEBUG] test episode 81: reward = 3648.17, steps = 1000
00:22:00 [DEBUG] test episode 82: reward = 3592.55, steps = 1000
00:23:02 [DEBUG] test episode 83: reward = 3598.26, steps = 1000
00:24:06 [DEBUG] test episode 84: reward = 3605.84, steps = 1000
00:24:08 [DEBUG] test episode 85: reward = -1.99, steps = 34
00:25:10 [DEBUG] test episode 86: reward = 3604.22, steps = 1000
00:26:16 [DEBUG] test episode 87: reward = 3606.47, steps = 1000
00:27:19 [DEBUG] test episode 88: reward = 3597.05, steps = 1000
00:28:23 [DEBUG] test episode 89: reward = 3591.54, steps = 1000
00:29:27 [DEBUG] test episode 90: reward = 3446.64, steps = 1000
00:30:30 [DEBUG] test episode 91: reward = 3614.64, steps = 1000
00:31:32 [DEBUG] test episode 92: reward = 3606.94, steps = 1000
00:32:36 [DEBUG] test episode 93: reward = 3417.76, steps = 1000
00:33:38 [DEBUG] test episode 94: reward = 3617.41, steps = 1000
00:34:42 [DEBUG] test episode 95: reward = 3593.12, steps = 1000
00:35:44 [DEBUG] test episode 96: reward = 3610.05, steps = 1000
00:36:47 [DEBUG] test episode 97: reward = 3614.57, steps = 1000
00:37:50 [DEBUG] test episode 98: reward = 3603.14, steps = 1000
00:38:52 [DEBUG] test episode 99: reward = 3592.79, steps = 1000
00:38:52 [INFO] average episode reward = 3210.89 ± 1116.04